Spacial targets such as communication satellites and resources satellites launched at home and abroad can be used in network communication, aerophotography, geodesy, etc. Targets in images obtained by sensors tend to be blurred due to limited spacial resolution of a camera and interference of random noise and atmospheric turbulence with a remote optical imaging system, which makes post operations such as target positioning and target categorizing quite difficult, and therefore, how to improve imaging quality of the images becomes a focus of research both at home and abroad. Intensive studies on deblurring algorithms on targets under the above imaging conditions have been carried out and relevant achievements have been made. For example, Turbulence-degraded Infrared Image Blind Restoration Method Based on Generalized Regularization, He Chengjian, Hong Hanyu and Zhang Tianxu, Infrared Technology, No. 8, Vol. 28, August 2006, proposes a generalized regularization method developed from a conventional nonlinear regularization which can realize correction effectively. However, the method mainly targets at infrared imaging in turbulence and correction on remote visible imaging is not covered. Blind Restoration of Turbulend-degraded Image Using Maximum Entropy Algorithm, Fu Changjun, Xu Dong and Zhao Yan, Infrared and Laser Engineering, No. 3 Vol 37, June 2008, approximates a nonlinear entropy expression by a two order polynomial to avoid complex computations caused by nonlinearity of maximum entropy regularized terms. Theoretical analysis shows accuracy of the approximation can be assured by density transformation and the problem can be solved by a conjugate method which needs less computation. Deblurring Poissonian Images by Split Bregman Techniques, S. Setzer, G. Steidl and T. Teuber, J. Vis. Commun. Image R. 21 (2010) 193˜199 proposes a rapid image deblurring method based on split Bregman techniques which can only improve velocity of the algorithm and fails to improve correction quality of the images.